• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.679-690
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• 2015

The need to remove the cause of traffic accidents by improving the engineering system for a vehicle and the road in order to minimize the accident hazard. This is likely to cause traffic accident continue to take a large and significant social cost and time to improve the reliability and efficiency of this generally poor road, thereby generating a lot of damage to the national traffic accident caused by improper environmental factors. In order to minimize damage from traffic accidents, the cause of accidents must be eliminated through technological improvements of vehicles and road systems. Generally, it is highly probable that traffic accident occurs more often on roads that lack safety measures, and can only be improved with tremendous time and costs. In particular, traffic accidents at intersections are on the rise due to inappropriate environmental factors, and are causing great losses for the nation as a whole. This study aims to present safety countermeasures against the cause of accidents by developing an intersection Traffic safety evaluation model. It will also diagnose vulnerable traffic points through BPA (Back -propagation algorithm) among artificial neural networks recently investigated in the area of artificial intelligence. Furthermore, it aims to pursue a more efficient traffic safety improvement project in terms of operating signalized intersections and establishing traffic safety policies. As a result of conducting this study, the mean square error approximate between the predicted values and actual measured values of traffic accidents derived from the BPA is estimated to be 3.89. It appeared that the BPA appeared to have excellent traffic safety evaluating abilities compared to the multiple regression model. In other words, The BPA can be effectively utilized in diagnosing and practical establishing transportation policy in the safety of actual signalized intersections.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.10
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• pp.709-721
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• 2006

The traditional broadcasting services over terrestrial, satellite and cable media have been unidirectional mass media regardless of TV viewer's preferences. Recently ich media streaming has become possible via the broadb and networks. Furthermore, since bidirectional communication is possible, personalcasting such as personalized streaming services has been emerging by taking into account the user's preference on content genres, viewing times and actors/actresses etc. Accordingly, personal media becomes an important means for content provision service in addition to the traditional broadcasting service as mass media. In this paper, we introduce a user profile reasoning method for target advertisement which is considered an important application in personalcasting service. The proposed user profile reasoning method predicts an unknown TV viewer's gender and ages by analyzing TV Viewing history data. Based on the estimated user's gender and ages, a target advertisement is provided with TV Anytime metadata. A proposed target advertisement system is developed based on the user profile reasoning and the target advertisement selection method. To show the effectiveness of our proposed methods, we present a plenty of experimental results by using realistic TV viewing history data.

• Journal of Korea Water Resources Association
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• v.46 no.7
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• pp.697-706
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• 2013

In recent, Korea has faced on water quality management problems in reservoir and river because of increasing water temperature and rainfall frequency caused by climate change. This study is effectively to manage water quality for establishment of algal bloom forecasting models with artificial neural network. Daecheong reservoir located in Geum river has suitable environment for algal bloom because it has lots of contaminants that are flowed by rainfall. By using back propagation algorithm of artificial neural networks (ANNs), a model has been built to forecast the algal bloom over short-term (1, 3, and 7 days). In the model, input factors considered the hydrologic and water quality factors in Daecheong reservoir were analyzed by cross correlation method. Through carrying out the analysis, input factors were selected for algal bloom forecasting model. As a result of this research, the short term algal bloom forecasting models showed minor errors in the prediction of the 1 day and the 3 days. Therefore, the models will be very useful and promising to control the water quality in various rivers.

• Proceedings of KOSOMES biannual meeting
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• 2005.11a
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• pp.75-78
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• 2005

The detectability of radar depends on RCS(radar cross section). The RCS for complex radar targets may be only approximately calculated by using low-frequency or high-frequency scattering methods, while the RCS for simple radar targets can be exactly obtained by applying on eigen-function method. However, the conventional methods for calculation of RCS are computationally complex. We propose an radar signal model for RCS calculation by MUSIC algorithm In this research, it is assumed that the radar target is considered as a ring of scatterers. The amplitudes of scatterers may be statistically distributed. As the result, the radar signal model is proposed to use MUSIC, and the RCS is calculated by a simple linear algebraic method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.1C
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• pp.64-71
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• 2003

In this paper, we proposed an algorithm that shorten coding time maintaining image quality in JPEG2000, which is the standard, of still image compression. This method encodes only the bit plane selected as appropriate truncation point for output bitstream, obtained from estimation of frequency distribution for whole image. Wavelet characterized by multi-resolution has vertical, horizontal, and diagonal frequency components for each resolution. The frequency interrelation addressed above is maintained thorough whole level of resolution and represents the unique frequency characteristics for input image. Thus, using the frequency relation at highest level, we can pick the truncation point for the compression time decrease by estimating code bits at encoding each code block. Also, we reduced the encoding time using simply down sampling instead of low-pass filtering at low-levels which are not encoded in color component of lower energy than luminance component. From the proposed algorithm, we can reduce about 15~36% of encoding time maintaining PSNR 30$\pm$0.5㏈.

• Geotechnical Engineering
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• v.10 no.4
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• pp.17-28
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• 1994

It is well known that human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basic of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully. In this study, error back propagation algorithm which is one of the teaching technique of artificial neural networks is applied to predict ultimate bearing capacity of pile foundations. For the verification of applicability of this system, a total of 28 data of model pile test results are used. The 9, 14 and 21 test data respectively out of the total 28 data are used for training the networks, and the others are used for the comparison between the predicted and the measured. The results show that the developed system can provide a good matching with model pile test results by training with data more than 14. These limited results show the possibility of utilizing the neural networks for pile capacity prediction problems.

• Journal of the Korean GEO-environmental Society
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• v.13 no.8
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• pp.25-33
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• 2012

Although it is important to reflect the accurate information of the ground condition in the tunnel design, the analysis and design are conducted by limited information because it is very difficult to consider various geographies and geotechnical conditions. When the tunnel is under construction, examination of accurate safety and prediction of behavior are overcome the limits of predicting behavior by Artificial Neural Network in this study. First, construct the suitable structure after the data of field was made sure by the multi-layer back propagation, then apply with algorithm. Employ the result of measured data from database, and consider the influence factor of tunnel, like supporting pattern, RMR, Q, the types of rock, excavation length, excavation shape, excavation over, to carry out the reliable analysis through field applicability of Artificial Neural Network. After studying, using the ANN model to predict the shearing displacement, convergence displacement, underground displacement, Rock bolt output follow the excavation over of tunnel construction field, then determine the field applicability with ANN through field measured value and comparison analysis when tunnel is being constructed.

• The Korean Journal of Petroleum Geology
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• v.11 no.1 s.12
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• pp.9-17
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• 2005

In the main target area of the block II, Targe-scale faults occur below the unconformity developed around 1 km in depth. The contrast of seismic velocity around the unconformity is generally so large that the strong multiples and the radical velocity variation would deteriorate the quality of migrated section due to serious distortion. More than 15 kinds of data processing techniques have been applied to improve the image resolution for the structures farmed from this active crustal activity. The bad and noisy traces were edited on the common shot gathers in the first step to get rid of acquisition problems which could take place from unfavorable conditions such as climatic change during data acquisition. Correction of amplitude attenuation caused from spherical divergence and inelastic attenuation has been also applied. Mild F/K filter was used to attenuate coherent noise such as guided waves and side scatters. Predictive deconvolution has been applied before stacking to remove peg-leg multiples and water reverberations. The velocity analysis process was conducted at every 2 km interval to analyze migration velocity, and it was iterated to get the high fidelity image. The strum noise caused from streamer was completely removed by applying predictive deconvolution in time space and ${\tau}-P$ domain. Residual multiples caused from thin layer or water bottom were eliminated through parabolic radon transform demultiple process. The migration using curved ray Kirchhoff-style algorithm has been applied to stack data. The velocity obtained after several iteration approach for MVA (migration velocity analysis) was used instead or DMO for the migration velocity. Using various testing methods, optimum seismic processing parameter can be obtained for structural and stratigraphic interpretation in the Block II, Yellow Sea Basin.

• Tunnel and Underground Space
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• v.17 no.4
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• pp.301-310
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• 2007

The prediction of temperature in the workings for the propriety examination for the development of a deep coal bed and the ventilation design is fairly important. It is quite demanding to obtain precise thermal conductivity of rock due to the variety and the complexity of the rock types contiguous to the coal bed. Therefore, to estimate the thermal conductivity corresponding to this geological situation and complex gallery conditions, a computing program which is TemPredict, is developed in this study. It employs Artificial Neural Network and calculates the climatic conditions in galleries. This advanced neural network is based upon the Back-Propagation Algorithm and composed of the input layers that are acceptant of the physical and geological factors of the coal bed and the hidden layers each of which has the 5 and 3 neurons. To verify TemPredict, the calculated result is compared with the measured one at the entrance of -300 ML 9X of Jang-sung production department, Jang-sung Coal Mine. The difference between the results calculated by TemPredict ($25.65^{\circ}C$) and measured ($25.7^{\circ}C$) is only $0.05^{\circ}C$, which is less than the allowable error 5%. The result has more than 95% of very high reliability. The temperature prediction for the main carriage gallery 9X in -425 ML under construction when it is completed is made. Its result is $28.2^{\circ}C$. In the future, it would contribute to the ventilation design for the mine and the underground structures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.3 no.1
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• pp.13-24
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• 1983

A mathematical model for the two-dimensional unsteady flow was developed by introducing Abbott's implicit finite difference operator and double sweep algorithm, which could be applied to simulate the respose of a harbor against the intrusion of long waves through the entrance connected to open sea. In order to improve its accuracy corresponding to the field phenomena, bottom resistance, Coriolis force, wind effect terms were included and wave direction and radiating effect was considered. The result of seiche test was always stable and the amplitude was accurate. Some phase shift was occured, but it could be reduced by using small values of Courant number and many points per a wave length as well. A comparision with the Ippen and Goda's theoritical and hydraulic experimental works was fulfilled.